Internet multimedia traffic classification from QoS perspective using semi-supervised dictionary learning models

To address the issue of finegrained classification of Internet multimedia traffic from a Quality of Service (QoS) perspective with a suitable granularity, this paper defines a new set of QoS classes and presents a modified K-Singular Value Decomposition (K-SVD) method for multimedia identification. After analyzing several instances of typical Internet multimedia traffic captured in a campus network, this paper defines a new set of QoS classes according to the difference in downstream/upstream rates and proposes a modified K-SVD method that can automatically search for underlying structural patterns in the QoS characteristic space. We define bag-QoS-words as the set of specific QoS local patterns, which can be expressed by core QoS characteristics. After the dictionary is constructed with an excess quantity of bag-QoS-words, Locality Constrained Feature Coding (LCFC) features of QoS classes are extracted. By associating a set of characteristics with a percentage of error, an objective function is formulated. In accordance with the modified K-SVD, Internet multimedia traffic can be classified into a corresponding QoS class with a linear Support Vector Machines (SVM) classifier. Our experimental results demonstrate the feasibility of the proposed classification method

One Video Stream to Serve Diverse Receivers

The fundamental problem of wireless video multicast is to scalably serve multiple receivers which may have very different channel characteristics. Ideally, one would like to broadcast a single stream that allows each receiver to benefit from all correctly received bits to improve its video quality. We introduce Digital Rain, a new approach to wireless video multicast that adapts to channel characteristics without any need for receiver feedback or variable codec rates. Users that capture more packets or have fewer bit errors naturally see higher video quality. Digital Rain departs from current approaches in two ways: 1) It allows a receiver to exploit video packets that may contain bit errors; 2) It builds on the theory of compressed sensing to develop robust video encoding and decoding algorithms that degrade smoothly with bit errors and packet loss. Implementation results from an indoor wireless testbed show that Digital Rain significantly improves the received video quality and the number of supported receivers

Efficient compression of motion compensated residuals

EThOS - Electronic Theses Online ServiceGBUnited Kingdo